arXiv 1703.033652017-03cs.LG

Learning Active Learning from Data

EPFL（洛桑联邦理工学院）；University of Bern（伯尔尼大学）

Ksenia Konyushkova, Raphael Sznitman, Pascal Fua

英文摘要：

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

IDEA：一种基于逆向领域专家自适应的主动DNN知识产权保护方法

IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method

School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer Science, Nanjing University of Information Science and Technology（教育部长数字取证工程研究中心、信息科学技术大学计算机学院）；School of Electrical and Electronic Engineering and National Integrated Centre for Evaluation (NiCE), Nanyang Technological University（南洋理工大学电子与电气工程学院和国家整合评估中心（NiCE））

本文提出IDEA，一种基于逆向领域专家自适应的主动DNN IP保护方法，通过混合专家模型和知识蒸馏实现主动授权与来源追溯，有效抵御未授权访问和IP侵权。

2025-05-30cs.AIcs.CRcs.CV

Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets

Johns Hopkins University（约翰斯·霍普金斯大学）；University of Delaware（特拉华大学）

Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behind the proposed InitPA method for addressing imbalance is to base cost models during AL on an estimate of overall corpus imbalance computed via a small unbiased sample rather than the imbalance in the lab…

2014-09-17cs.CLcs.LGstat.ML

通过多目标优化与主动学习实现材料全自主设计的框架：挑战与下一步

A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

Argonne National Laboratory（阿贡国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）

本文提出一种结合多目标黑盒优化与持续更新机器学习模型的主动学习框架，用于自驱动实验室中的智能采样，并在连续流化学实验中验证其能自主确定电解质的最佳制造条件。

2023-04-15cs.LG

青少年心理健康的数字表型：一项利用机器学习从主动和被动智能手机数据预测心理健康风险的可行性研究

Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

Imperial（帝国理工学院）；Universität Bayreuth（拜罗伊特大学）

本研究针对青少年精神障碍早发、求助率低的问题，采用结合对比预训练与监督微调的机器学习框架，整合智能手机主动与被动数据预测多种心理健康风险，验证了该方案的可行性与有效性。

2025-01-15cs.AIcs.LG

SQBC：利用大语言模型生成的合成数据开展在线政治讨论立场检测的主动学习

SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions

Heinrich Heine University Düsseldorf（海因里希·海涅杜塞尔多夫大学）；Technical University of Dortmund（多特蒙德工业大学）

针对立场检测模型标注数据不足的问题，本研究提出用LLM合成数据扩充小型微调数据集，还提出基于委员会查询的SQBC主动学习方法，以合成数据为预言机筛选高价值未标注样本，实验证明两种方法均能提升性能，主动筛选样本微调效果甚至超完整数据集。

2024-04-11cs.AIcs.CLcs.LG

Rethinking deep active learning: Using unlabeled data at model training

Inria, IRISA, Univ Rennes, CNRS（法国国家信息与自动化研究所、法国国家科学研究中心、雷恩大学、IRISA研究所）

Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for acquisition. In this work we depart from this setting by using both labeled and unlabeled data during model training across active learning cycles. We do so by using unsupervised feature learning at the beginning of the active learning pipeline and semi-supervised learning at every active learning cycle, on all available data. The former has not been investigated before in active learning, while the study of latter in the context of deep learning is scarce and recent find…

2019-11-19cs.CVcs.LG

更多相关论文收起更多论文

Active Learning of SVDD Hyperparameter Values

Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Aarhus University（奥胡斯大学）

Support Vector Data Description is a popular method for outlier detection. However, its usefulness largely depends on selecting good hyperparameter values -- a difficult problem that has received significant attention in literature. Existing methods to estimate hyperparameter values are purely heuristic, and the conditions under which they work well are unclear. In this article, we propose LAMA (Local Active Min-Max Alignment), the first principled approach to estimate SVDD hyperparameter values by active learning. The core idea bases on kernel alignment, which we adapt to active learning with…

2019-12-04cs.LGstat.ML

A Simple yet Brisk and Efficient Active Learning Platform for Text Classification

American Family Insurance（美国家庭保险）

In this work, we propose the use of a fully managed machine learning service, which utilizes active learning to directly build models from unstructured data. With this tool, business users can quickly and easily build machine learning models and then directly deploy them into a production ready hosted environment without much involvement from data scientists. Our approach leverages state-of-the-art text representation like OpenAI's GPT2 and a fast implementation of the active learning workflow that relies on a simple construction of incremental learning using linear models, thus providing a br…

2021-01-31cs.HCcs.IRcs.LG

Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection

University of California Berkeley（加州大学伯克利分校）；University of California San Francisco School of Medicine（加州大学旧金山分校医学院）

Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets. However, the enormous cost of labeling medical data makes this challenging. In this paper, we build a cost-sensitive active learning system for the problem of intracranial hemorrhage detection and segmentation on head computed tomography (CT). We show that our ensemble method compares favorably with the state-of-the-art, while running faster and using less memory. Moreover, our experiments are done using a substantially larger dataset than earlier papers on …

2018-09-08cs.CVcs.LG

Active WeaSuL: Improving Weak Supervision with Active Learning

ING Wholesale Banking Advanced Analytics（ING批发银行高级分析部）；University of Amsterdam（阿姆斯特丹大学）；Feebris（Feebris公司）

The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expert-defined rules $\boldsymbolλ$ to estimate probabilistic labels $p(y|\boldsymbolλ)$ for the entire data set. These rules, however, are dependent on what experts know about the problem, and hence may be inaccurate or may fail to capture important parts of the problem-space. To mitigate this, we propose Active WeaSuL: an approach that incorporates active learning into weak supervision. In Active WeaSuL, experts do not only define rules, bu…

2021-04-30cs.LGstat.ML

GALAXY：极端场景下的基于图的主动学习

GALAXY: Graph-based Active Learning at the Extreme

University of Wisconsin, Madison（威斯康星大学麦迪逊分校）

针对开放世界中的极端类别不平衡问题，提出融合图主动学习与深度学习的 GALAXY 策略，以自适应选择更类别平衡的样本，并在视觉分类实验中优于现有深度主动学习方法。

2022-05-27cs.AIcs.CVcs.LG

Active Learning for Deep Neural Networks on Edge Devices

Idein inc.（Idein公司）

When dealing with deep neural network (DNN) applications on edge devices, continuously updating the model is important. Although updating a model with real incoming data is ideal, using all of them is not always feasible due to limits, such as labeling and communication costs. Thus, it is necessary to filter and select the data to use for training (i.e., active learning) on the device. In this paper, we formalize a practical active learning problem for DNNs on edge devices and propose a general task-agnostic framework to tackle this problem, which reduces it to a stream submodular maximization…

2023-03-22cs.CVcs.LGcs.NI